pv_rcnn

PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection

PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection

Introduction

3D object detection has been receiving increasing attention from both industry and academia thanks to its wide applications in various fields such as autonomous driving and robotics. LiDAR sensors are widely adopted in autonomous driving vehicles and robots for capturing 3D scene information as sparse and irregular point clouds, which provide vital cues for 3D scene perception and understanding. In this paper, we propose to achieve high performance 3D object detection by designing novel point-voxel integrated networks to learn better 3D features from irregular point clouds.

Results and models

KITTI

Backbone	Class	Lr schd	Mem (GB)	Inf time (fps)	mAP	Download
SECFPN	3 Class	cyclic 80e	5.4		72.28	model \ log

Note: mAP represents AP11 results on 3 Class under the moderate setting.

Detailed performance on KITTI 3D detection (3D) is as follows, evaluated by AP11 metric:

	Easy	Moderate	Hard
Car	89.20	83.72	78.79
Pedestrian	66.64	59.84	55.33
Cyclist	87.25	73.27	69.61

Citation

@article{ShaoshuaiShi2020PVRCNNPF,
  title={PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection},
  author={Shaoshuai Shi and Chaoxu Guo and Li Jiang and Zhe Wang and Jianping Shi and Xiaogang Wang and Hongsheng Li},
  journal={computer vision and pattern recognition},
  year={2020}
}